Comprehensive evaluation of privacy policies using the contextual integrity framework
Bibliographic record
Abstract
Abstract Online privacy policies are often lengthy and difficult to understand. This may lead many users to avoid reading them despite increasing concerns about how their personal information is managed. This article presents a structured approach to evaluate the transparency and comprehensiveness of privacy policies using a comprehensive set of evaluation questions within the contextual integrity (CI) framework. We use these questions to identify policies' responses to key privacy concerns. Applying the CI framework, we analyze the clarity and context of these responses, identifying any vagueness and contextual issues that could impede a user's understanding of the privacy policy. Using the CI analysis, we quantify the quality of policies' responses, thereby enabling users to make informed decisions about online services or products. We apply our methodology to two popular messaging apps, Telegram and WhatsApp, using them as case studies to systematically uncover the strengths and weaknesses of their privacy policies. The findings demonstrate that our proposed methodology can effectively identify transparency issues and assess the comprehensiveness of privacy policies. This suggests that our approach could serve as a practical alternative to subjective evaluations typically conducted by privacy experts.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.111 | 0.199 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".